Data Hack Tuesday
Tips & Tricks for Your Data
Brought to you every Tuesday.
Business Intelligence with LLMs: An Engineer’s Guide to Leveraging Large Language Models
November 7, 2023
In this article, we’re going to look into the world of Business Intelligence (BI), with a practical emphasis on how engineers can use LLMs to make sense of data, get insights, and optimize their BI operations.View More
Binary Search — Better Algorithms for Better Design
October 24, 2023
In this technical post, we will look at the practical applications and principles of binary search, emphasizing its importance in software and system design optimization.View More
Bridging the Software Development Gap with KBSE
October 10, 2023
KBSE is positioned to play a crucial part in determining the future of software engineering as businesses strive for software development that is quicker, more effective, and error-free.View More
Building a Robust Software Development Lifecycle (SDLC) with IoT in Mind
September 26, 2023
In this article, we will examine the essential components of developing a solid SDLC with the Internet of Things, with a focus on the significance of strategy, security, testing, and scalability.View More
How To Visualize Word Embeddings with Google Embedding Projector in Tensorboard
September 12, 2023
In this article, you will also learn how to use Google Embedding Projector to visualize word embeddings.View More
Low-Code and No-Code: A New Era of Software Development
August 15, 2023
Low-code and no-code development have ushered in a new era of software development, revolutionizing how applications are used in business.View More
Extract, Transform, and Load: A Complete Guide for Beginners
April 11, 2023
What is ETL? Extraction, Transformation, and Load (ETL) is the process of moving data from various sources to a target source, like a data warehouse. It involves extracting data, transforming it for compatibility, and loading it into the desired destination.View More
An Introduction to Time Series Analysis
March 14, 2023
Unlocking Time’s Secrets: Discover how time series analysis sheds light on historical events, aiding in understanding their causes and effects.
Time Series and AI: Dive into the synergy of time series and artificial intelligence, enabling us to forecast stock prices, weather, and more.
Components Unveiled: Explore the four key components of time series analysis – Trend, Seasonality, Cyclical, and Irregularity, and their roles in deciphering data patterns.
When Not to Use It: Learn when time series analysis is not suitable for static data, small sample sizes, or nonlinear relationships.
Stationarity Matters: Delve into the significance of stationarity in time series, enabling accurate predictions and the use of statistical techniques.
Unsupervised Learning Explained Using K-Means Clustering
March 7, 2023
“Machine learning models, like humans, can learn patterns in data in a variety of ways. There are two main methods of learning: supervised and unsupervised learning. These learning methods, similar to humans, may be great for some use cases but may not be as effective when applied to other problems.”
“Supervised Learning: This training method involves feeding labeled data to the machine learning algorithm and allowing it to find patterns in the data. Labeled data is data that has a tag, or, better yet- a description. In essence, the algorithm understands the meaning of the data or its relevance.”
“Unsupervised Learning: Unsupervised learning is the polar opposite of supervised learning. Knowing this, you’ll understand that it entails training machine learning algorithms on unlabeled data. It is unlabeled because it has no tag or description. The goal of unsupervised learning is to find patterns in data and classify it into different sets based on similarities.”
“K-Means Clustering: K-Means clustering is an unsupervised machine learning algorithm that groups similar data points together into clusters based on similarities. The value of K determines the number of clusters. K-Means clustering is a form of partitional clustering, which separates a data set into sets of separate clusters.”View More